ITR - (EVS+NHS) - (dmc + int): Knowledge Infusion

ITR - (EVS NHS) - (dmc int)：知识注入

• 批准号: 0427129
• 负责人: Leslie Valiant
• 金额: $ 90.8万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0427129&HistoricalAwards=false
• 关键词: ITR; EVS+NHS; dmc; int; Knowledge

The goal of the proposed research is to extend the reach of machine learning technology so that it can enable computers to perform a broader span of tasks than currently. In particular the goal is to enable machines to extract knowledge from data into a form such that robust reasoning can be done on it. Currently representations on which reasoning can be done on a large scale are typically programmed and the results suffer from brittleness - they do not behave well in unforeseen situations. In the proposed approach, which is called knowledge infusion, the goal is to acquire the rules on which reasoning will be done by a combination of programming and learning, and to have a continuous process of learning and checking against an environment to ensure that the rules are reliable. The goal is to handle unaxiomatized or commonsense knowledge, which encompasses the bulk of knowledge that humans handle everyday, as embodied in speech or text, replete as these often are with inconsistencies, ambiguities and errors. This can be distinguished from knowledge that is known to be axiomatizable, such as most knowledge of a mathematical nature. Axiomatized knowledge, in general, can be easily programmed, and computers can usually fully exploit such knowledge up to any inherent computational complexity limitations of the problem at hand. The most central aims of the research are the development of algorithms that realize knowledge infusion and are computationally efficient and effective even for very large datasets. Also central is the identification of what the fundamental limits of the phenomenon are. The techniques used will be from theoretical computer science, and experimentation on large datasets will be carried out as needed. The goal is to be able to infuse into machines commonsense knowledge about the world on a large scale and in a way such that the machines will be able to reason with it with a controlled level of robustness. Success in this endeavor can be expected to have applications in almost all areas of computing that involve either human interaction with a computer, or computation on data that was generated by or has reference to humans. Hence there are numerous connections with the national priority areas EVS and NHS, and with the technical focus areas int and dmc. Broader Impact: If successful the results of the research will help enhance the effectiveness of computers to handle commonsense or unaxiomatized information about the world. This would extend the usefulness of computers to new areas and contribute to prosperity (EVS). It would also enable large datasets to be analyzed automatically with greater functionality than hitherto (NHS).

拟议研究的目标是扩大机器学习技术的范围，使计算机能够执行比目前更广泛的任务。特别是，目标是使机器能够从数据中提取知识并形成某种形式，以便可以对其进行稳健的推理。目前，可以进行大规模推理的表示通常是编程的，并且结果很脆弱——它们在不可预见的情况下表现不佳。在所提出的称为知识注入的方法中，目标是通过编程和学习的结合来获取推理的规则，并针对环境进行连续的学习和检查过程，以确保规则是可靠的。目标是处理非公理化或常识性知识，其中包含人类日常处理的大量知识，如语音或文本中所体现的那样，这些知识通常充满了不一致、模糊性和错误。这可以与已知可公理化的知识区分开来，例如大多数数学性质的知识。一般来说，公理化知识可以很容易地编程，并且计算机通常可以充分利用这些知识，直至解决当前问题的任何固有的计算复杂性限制。 该研究最核心的目标是开发实现知识注入的算法，即使对于非常大的数据集，其计算效率也很高。同样重要的是确定该现象的基本限制是什么。所使用的技术将来自理论计算机科学，并将根据需要对大型数据集进行实验。 目标是能够大规模地将有关世界的常识知识注入机器中，使机器能够以受控的鲁棒性水平进行推理。这一努力的成功预计将在几乎所有涉及人类与计算机交互或涉及人类生成或涉及人类的数据计算的计算领域中得到应用。因此，与国家优先领域 EVS 和 NHS 以及技术重点领域 int 和 dmc 之间存在着众多联系。 更广泛的影响：如果成功，研究结果将有助于提高计算机处理有关世界的常识或非公理化信息的效率。这将把计算机的用途扩展到新的领域并促进繁荣（EVS）。它还将使大型数据集能够以比迄今为止（NHS）更强大的功能进行自动分析。